Global Market Investigation for a New Product in Video Post-Production : Case Company: Loupedeck Ltd
Bibliographic record
Abstract
This study is a research-orientated thesis which is focused on creating a knowledge basis of the video post-production industry for the company Loupedeck and its new product development project. The company offers an innovative and advanced control panel to improve and facilitate the professional photo editing workflow in Adobe Lightroom.\nLoupedeck is growing and opening new market opportunities, therefore, they are aiming to design and manufacture more hardware devices for post processing solutions in the future. The objective of the thesis is to conduct a market study and customer needs investigation for the start of the new product development process. The research questions are designed to investigate the current market of video editing hardware and software, industry trends, target audience and their needs and preferences for the new product.\n\nThe literature review consists of the product development concept and detailed description of its stages, market study and its elements, such as market segmentation, target audience, competition analysis and market trends. Additionally, several strategies of customer interaction in the development process are reviewed and the process of customer needs data collecting and analyzing is investigated.\n\nThe study includes primary and secondary data. The selected methodology for the primary data collection is qualitative research through in-depth semi-structured interviews. The secondary data is gathered through the desk research approach.\n\nThe findings chapter presents the summary of the video post-production industry and the most commonly used video editing software on the market. It was defined that Adobe Premiere Pro and Final Cut Pro have the largest share of the video post processing soft-ware market. The chapter shows the estimated number of business users of these two applications and gives statistical data of the video editors amount in US and UK markets. It was defined that the first five leading markets of both software are: US, UK, Canada, Australia and France. I determined that the UK video post-production industry annual growth increased by 4.3% and the US market increased by 2.7%. On the global level, market analysists predicted that the post-production market will grow at a Compound Annual Growth Rate of almost 6% by 2021.The study findings also include the overview of the existing hardware products. I defined seven potential competitors on the global market. Moreover, the research provides the description of potential customers profiles and analysis of the customer needs regarding the video post-production workflow.\n\nIn conclusion, I discuss the study results and the research in terms of validity, reliability and limitations, answers research questions and provide recommendations for future research. The personal learning outcomes are precisely described in the last chapter of the thesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".